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Electricity demand uncertainty modeling using enhanced path-based scenario generation method

Commerce; Deregulation; Electric power utilization; Errors; Gaussian noise (electronic); Power markets; Uncertainty analysis; Autoregressive moving average; Autoregressive moving average method; Day ahead market; Deregulated electricity market; Electricity demands; Mean absolute percentage error; Sc...

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Main Authors: Tahmasebi M., Pasupuleti J.
Other Authors: 55945605900
Format: Conference Paper
Published: Institute of Electrical and Electronics Engineers Inc. 2023
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spelling my.uniten.dspace-231392023-05-29T14:37:57Z Electricity demand uncertainty modeling using enhanced path-based scenario generation method Tahmasebi M. Pasupuleti J. 55945605900 11340187300 Commerce; Deregulation; Electric power utilization; Errors; Gaussian noise (electronic); Power markets; Uncertainty analysis; Autoregressive moving average; Autoregressive moving average method; Day ahead market; Deregulated electricity market; Electricity demands; Mean absolute percentage error; Scenario generation; Uncertainty; Electric power generation One of the most important realities and uncertainties in the deregulated electricity market is electricity demand. Electricity demand scenario generation in day-ahead markets using newly proposed Enhanced path-based scenario generation method based on autoregressive moving average is developed in this paper. A new enhanced path-based scenario generation method to generate scenarios of the random variable and uncertainties modeling to achieve lower mean absolute percentage error for scenario generation compared to path-based autoregressive moving average method is proposed. Comparison of expected values obtained from the proposed method and path-based ARMA method, as well as real values, shows lower mean absolute percentage error for proposed method. It is observed that the mean absolute percentage error is decreased 5% for electricity demand using newly proposed scenario generation method. Lower mean absolute percentage error indicates higher accuracy of this method for generation of scenarios. � 2017 IEEE. Final 2023-05-29T06:37:57Z 2023-05-29T06:37:57Z 2017 Conference Paper 10.1109/IYCE.2017.8003747 2-s2.0-85030167442 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85030167442&doi=10.1109%2fIYCE.2017.8003747&partnerID=40&md5=f34999859e0c57253e820d98d1a641ca https://irepository.uniten.edu.my/handle/123456789/23139 8003747 Institute of Electrical and Electronics Engineers Inc. Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Commerce; Deregulation; Electric power utilization; Errors; Gaussian noise (electronic); Power markets; Uncertainty analysis; Autoregressive moving average; Autoregressive moving average method; Day ahead market; Deregulated electricity market; Electricity demands; Mean absolute percentage error; Scenario generation; Uncertainty; Electric power generation
author2 55945605900
author_facet 55945605900
Tahmasebi M.
Pasupuleti J.
format Conference Paper
author Tahmasebi M.
Pasupuleti J.
spellingShingle Tahmasebi M.
Pasupuleti J.
Electricity demand uncertainty modeling using enhanced path-based scenario generation method
author_sort Tahmasebi M.
title Electricity demand uncertainty modeling using enhanced path-based scenario generation method
title_short Electricity demand uncertainty modeling using enhanced path-based scenario generation method
title_full Electricity demand uncertainty modeling using enhanced path-based scenario generation method
title_fullStr Electricity demand uncertainty modeling using enhanced path-based scenario generation method
title_full_unstemmed Electricity demand uncertainty modeling using enhanced path-based scenario generation method
title_sort electricity demand uncertainty modeling using enhanced path-based scenario generation method
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2023
_version_ 1806426655810387968
score 13.222552

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